Optimizing risk-based breast cancer screening policies with reinforcement learning

Optimizing risk-based breast cancer screening policies with reinforcement learning

Screening programs must balance the benefit of early detection with the cost of overscreening. Here, we introduce a novel reinforcement learning-based framework for personalized screening, Tempo, and demonstrate its efficacy in the context of breast cancer. We trained our risk-based screening polici...

Full description

Bibliographic Details
Main Authors: Yala, Adam, Mikhael, Peter G, Lehman, Constance, Lin, Gigin, Strand, Fredrik, Wan, Yung-Liang, Hughes, Kevin, Satuluru, Siddharth, Kim, Thomas, Banerjee, Imon, Gichoya, Judy, Trivedi, Hari, Barzilay, Regina
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Format: Article
Language:English
Published: Springer Science and Business Media LLC 2022
Online Access:https://hdl.handle.net/1721.1/142737

Internet

https://hdl.handle.net/1721.1/142737

Similar Items